Issue 12, 2021

Selective-sampling Raman imaging techniques for ex vivo assessment of surgical margins in cancer surgery

Abstract

One of the main challenges in cancer surgery is to ensure the complete excision of the tumour while sparing as much healthy tissue as possible. Histopathology, the gold-standard technique used to assess the surgical margins on the excised tissue, is often impractical for intra-operative use because of the time-consuming tissue cryo-sectioning and staining, and availability of histopathologists to assess stained tissue sections. Raman micro-spectroscopy is a powerful technique that can detect microscopic residual tumours on ex vivo tissue samples with accuracy, based entirely on intrinsic chemical differences. However, raster-scanning Raman micro-spectroscopy is a slow imaging technique that typically requires long data acquisition times wich are impractical for intra-operative use. Selective-sampling Raman imaging overcomes these limitations by using information regarding the spatial properties of the tissue to reduce the number of Raman spectra. This paper reviews the latest advances in selective-sampling Raman techniques and applications, mainly based on multimodal optical imaging. We also highlight the latest results of clinical integration of a prototype device for non-melanoma skin cancer. These promising results indicate the potential impact of Raman spectroscopy for providing fast and objective assessment of surgical margins, helping surgeons ensure the complete removal of tumour cells while sparing as much healthy tissue as possible.

Graphical abstract: Selective-sampling Raman imaging techniques for ex vivo assessment of surgical margins in cancer surgery

Article information

Article type
Critical Review
Submitted
17 Febr. 2021
Accepted
26 Apr. 2021
First published
12 Maijs 2021
This article is Open Access
Creative Commons BY license

Analyst, 2021,146, 3799-3809

Selective-sampling Raman imaging techniques for ex vivo assessment of surgical margins in cancer surgery

M. G. Lizio, R. Boitor and I. Notingher, Analyst, 2021, 146, 3799 DOI: 10.1039/D1AN00296A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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